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Compared to a constant model, the improvement in baseline length and station coordinate repeatabilities was on average between 2 and 3 % for all observational data considered.
With respect to the results obtained assuming the EGM96 model, the improvement reaches 10 50% both in the values of orbital RMS, and the station coordinates.
On the other hand, for dataset 868, where the physicochemical descriptor-based model outperforms the fingerprint-based model, the improvement is 23, 42 and 71% for cost levels 6, 10 and 14, respectively.
C2/CFB significantly influences AMD susceptibility and although accounting for effects at this locus does not dramatically increase the overall accuracy of the genetic risk model, the improvement over the CFH-LOC387715 model is statistically significant.
Furthermore, we would like to model the improvement relative to baseline disease activity, since disease activity at baseline influences the effectiveness of the intervention [ 27].
In the model, the improvement score (difference between the post-test and pre-test result) was entered as the outcome variable, while year and Group (A, B, C or D) were entered as predictor variables.
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The application of ensemble modeling has been shown to outperform single machine learning models, the improvement being larger if the three most predictive GBM, RF and SVM models are combined in the same ensemble (Table 2D and Figure 4).
As a result of the model validation, the improvement of the model discretization is outlined along with the calibration of the heat transfer coefficients.
Our model assumes the improvement of technology over time to be linear.
Three ESDs are added to the model for the improvement of the control.
When a model fails, the improvement will be set as 0. The key factors are as follows.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com